hammer-cost-of-care-case Scope: world hammer-coverage-intelligence vintage 2026-10-01 Revision SHA-256: 2f5a98584167f25bd74b6a978f821e3754da43c47c7aa8e722491c1f0648a4fe --- name: hammer-cost-of-care-case description: Use when a plan-side Cost of Care analyst asks whether a use case is worth a slot, what a digital solution saves the plan, how to size an exposure over a declared population, whether a saving is hard or soft, what regression to the mean does to a selected cohort's next period, or anything phrased in PMPM, line of business, funding type or minimum impact threshold. Opens with the refusal that decides these sessions, the paid-amount wall: this world holds no paid amounts and cannot produce a Cost of Care savings figure that would pass the actuarial review a business case goes through. Carries the medical-cost lever tree with this world's position marked on it, the population chain from line of business down to funding type and why fully insured against administrative services only decides who carries the risk, which calculator serves which step of a business case, and the refusals to expect with what each one is telling you. Then the assembly order: name the branch, classify the mechanism before modelling it because hard and soft are different models rather than one model with a label, pin the population, size the savings side as a unit cost times a volume times a rate of change and say which of the three came from where, size the cost side that weak submissions omit (integration, stand-up, member outreach, recurring run cost), and put it on a clock, because a mid-year launch carries a full year of cost against a fraction of a year of savings. Ends on the rates that decide a soft case, which this world holds none of, and the rule that a readmission rate means nothing without its cause rule and time window. Read hammer-savings-classification for the hard and soft definitions in full, and hammer-coverage-navigate for tool-level routing; this is the plan-side framing over both. --- # hammer-cost-of-care-case This file serves the plan-side Cost of Care analyst: the person deciding whether a use case earns a slot, holding it to a minimum impact threshold, and answerable to an actuarial review of whatever figure survives. If you are a vendor building the case that analyst will read, `hammer-vendor-evidence` is your file; this one is the standard your submission will be held to. **`tools/tools.json` is the authority on what exists and what each tool takes.** This file is the framing between the analyst's question and that surface. Where the two disagree, the tool surface is right and this is stale. ## The wall, before anything else **This world holds no paid amounts.** A Cost of Care standard scores savings on paid amounts, as opposed to allowed amounts or billed charges, and every money-bearing corpus here holds something else. Payer MRFs hold negotiated rates: what a payer published it pays when it pays, never what moved on an adjudicated claim. Hospital 45 CFR 180 filings hold filed charges, filed counterparty rates, filed brackets and cash prices: what a hospital published about its own prices. A Cost of Care savings figure cannot be derived from any of them, by any method, including arithmetic done by hand. So the position, stated before it is discovered mid-session: **this world can size an exposure and cannot produce the savings figure.** A figure built from negotiated rates and presented as Cost of Care savings is exactly the artefact an actuarial review exists to reject, and the refusal names what would settle it: adjudicated claims, which this world does not hold and says so. Everything below is what remains once that sentence is accepted, and it is most of the business case. ## The lever tree, and where this world sits on it A plan attacks PMPM medical cost through a decomposition like this, and the branch a question lives on decides whether this world can serve it. This section is the authoritative copy of that routing structure for this world; `hammer-vendor-evidence` points here rather than carrying a second one. ``` Reduce PMPM medical costs ├── Gross savings, improve population health │ ├── Improve Rx outcomes while managing cost → pharmacy │ └── Reduce or redirect LEGITIMATE demand → care management └── Cost savings, reduce cost of care ├── Reduce NON-LEGITIMATE demand → utilisation management ├── Reduce unit costs → provider network management │ ├── navigate to preferred providers or place of service │ └── negotiate contracted pricing terms └── Reduce over-payments of claims → claims administration ``` **This world sits on `reduce unit costs`, and only its contracted-pricing leaf.** What payers publish that they pay is contracted pricing evidence, and that is the whole of the position. Branch by branch, for every leaf it does not serve, the reason is one of three different facts, and they should not be flattened into "not supported": - **Pharmacy**: an absent corpus. The world holds no pharmacy source in any state, so there is nothing here to bound a drug-cost question with, and a refusal on this branch means nobody packed the data, not that the question is unanswerable in principle. - **Legitimate demand, and non-legitimate demand**: a kind of question this corpus cannot settle. Demand is utilisation, utilisation is adjudicated claims, and this corpus holds prices rather than events. A price says nothing about how often anything happens. - **Navigate to preferred providers or place of service**: the corpus cannot settle it, twice over. Steering needs a per-provider payment prediction and a ranking of providers, and both are refused: provider rows are observations of contracted rates, and 30 to 60 percent of small-cell variation is sampling error before anything else is said (the manifest's own `cannot_answer` entry carries that figure). And the saving from a redirect is a paid-amount question, which is the wall above. - **Over-payments of claims**: part absent corpus, part licence wall. Finding an overpayment needs the adjudications this world does not hold; and the bundling-edit half of that work runs into a licence wall, because the Medicare NCCI PTP baseline sits behind an AMA CPT licence acceptance form, so `stack_check` reads the Medicaid edition and says so rather than serving a Medicare verdict. A plan asking a question from any branch this world does not serve should be told which branch it came from and why the answer is not here, rather than handed an adjacent figure that happens to exist. ## The population chain, and who carries the risk The analyst's population grammar runs line of business, then management business unit, then state, then funding type, and the last link is the one that decides whose money a saving is. **A fully insured population is one the plan carries medical risk for; an administrative-services-only population is one the employer carries**, with the plan administering claims for a fee. A PMPM saving modelled against an ASO book accrues to the employer and not to the plan, so the same arithmetic is a different business case depending on that one declaration. No compartment in this world expresses this vocabulary, so the chain is the caller's to declare, all the way down, and a figure served without it is a figure about an undeclared population. The same holds for PMPM itself: **PMPM is not a figure this world holds**, and any chain that multiplies by it is multiplying by the caller's own declared assumption, which the answer will then carry as its basis. That is not a defect to route around; it is the difference between an exposure this world can frame and a book figure only the plan can supply. ## Which calculator serves which step The arithmetic of a business case largely exists here already, and each piece is the mechanical form of a line in the analyst's own template: - **The base population's condition figures**: `cohort_figures`, which serves headcounts and modelled prevalence with the measurement class attached, because an administrative count and an epidemiological rate are different quantities that share a slot. - **The screening or detection funnel**: `screening_yield`, declared sensitivity, specificity, prevalence with its class, and population. It is the step savings models skip, and it is where positive predictive value collapses at low prevalence. - **Hard against soft savings**: `deferred_versus_avoided` IS that distinction, made mechanical. A cost that never occurs under the intervention is an avoided cost; a cost that still occurs later is a deferred one; the tool totals them separately and refuses to sum them, because a timing shift added to a prevented event reports a saving that does not exist. - **The regression rate**: `regression_to_mean_expectation` IS the regression-to-the-mean assumption a target-population template carries, computed rather than picked. Declare the cohort mean, the selected cohort's mean and the measure's reliability, and it serves what the selected cohort measures next period with nothing done to it. `measure_reliability` supplies the reliability input from a numerator, a denominator and a declared between-provider variance. - **The unit-cost evidence itself**: `rate_position` and `rate_comparison`, with the thinness figures read before any cell is quoted, and the payer mix declared before anything is averaged. - **Quality dollars, where the case leans on them**: `withhold_earnback` for a withheld pool released against a menu, `tournament_position` for a cut set from the year's field, `competing_event_coupling` where a denominator is a survivor count that mortality moves. - **The verdicts**: `claim_check` for anything writable as arithmetic over declared figures, `judgement_check` for prose. Build the claim list before the prose, and write only what came back entailed. ## Building the model, in the order the work actually goes The section above is a parts list. This is the assembly order, and the order matters: three of these steps change the shape of the model rather than filling a cell in it, and doing them after the arithmetic has started means redoing the arithmetic. **1. Name the branch before anything else.** Take the question to the lever tree above and say which leaf it sits on. If it is not the contracted-pricing leaf, say so in the first paragraph rather than the last, and say which of the three reasons applies: absent corpus, a kind of question this corpus cannot settle, or a licence wall. A case that discovers its own branch halfway through has already spent effort on arithmetic nobody will read. **2. Classify the mechanism before you model it.** `savings_methodology` returns hard or soft and the evidence class that follows. `hammer-savings-classification` carries the definitions in full. This is step two and not step six because the classification decides what the model must contain: a hard case is held to adjudicated claims against a prior-period run rate, and a soft case is held to a declared counterfactual with every rate carrying its basis. Those are different models, not the same model with a different label. **3. Pin the population, all the way down to funding type.** Line of business, management business unit, state, funding type, condition, and the share the solution actually reaches. **A case that omits funding type has not said whose money it saves**, because a PMPM reduction on a fully insured book accrues to the plan and the same reduction on an administrative-services-only book accrues to the employer. None of this vocabulary exists in any compartment here, so all of it is the caller's declaration and travels as `assumption`. **4. Size the savings side, which is three factors and never one.** A savings figure is a **unit cost**, a **volume**, and a **rate of change** applied to them. Say which of the three came from where, because they do not come from the same place and this world can only reach one of them: - The **unit cost** is the factor this world speaks to, through `rate_position` and `rate_comparison`, and it speaks to it as a negotiated rate rather than a paid amount. Read the thinness figures before quoting a cell and declare the payer mix before averaging anything. - The **volume** is utilisation, utilisation is events, and this corpus holds prices rather than events. It is the plan's own figure, declared. - The **rate of change** is the redirect rate, the reduction rate, or the impact rate, and it is the factor that decides a soft case. This world holds none of them. See the section below. **5. Size the cost side, which is the half that gets left out.** A case with a modelled saving and no cost line is not a business case, it is a savings estimate. The cost side is at least four things: - **Integration**, the engineering to connect the solution to the systems it reads and writes. - **Initial stand-up**, the one-off business cost of launching: staffing, configuration, training. - **Member outreach**, which is the line most often missing and often the one that decides whether a case clears its bar. A solution nobody is told about has a utilisation rate near zero, so an outreach cost is not optional spending, it is what makes the reach assumption in step 3 true. - **Recurring run cost**, technical staff, licences and support, every year the saving is claimed. This world serves none of these figures and there is no refusal to quote here, because there is no tool to refuse: they are the caller's, and they belong in the claim report under `asserted without a check` like every other declaration. **6. Put it on a clock, then compare it to the bar.** Two things break a case that is arithmetically correct: - **Ramp.** A solution live from the middle of a year captures a fraction of a year of savings while carrying a full year of cost, so a strong multi-year case can show a negative first year. Report the first year and the multi-year figure separately. Reporting only the flattering one is the defect a reviewer is looking for. - **The threshold.** The question a plan is actually asking is not whether a solution saves anything. It has more candidates than slots, so the question is whether this one clears the bar for a slot. `candidate_screen` partitions declared candidates and never scores them, which is the right shape: the bar is the plan's own and this world does not hold it. ## The rates that decide a soft case are not here, and that is the finding Redirect rates, avoidable-encounter shares and impact rates are the factor that decides a gross-savings case, and **this world holds none of them.** They circulate as one plan's internal planning assumptions, and an assumption borrowed from somebody else's planning document is not a measurement of your population. So they are declared and carried as `assumption`, or cited to a source the reader can obtain and carried as `cited`. Declaring one `measured` is refused by name. That refusal is the most useful thing this world does to a business case, because a borrowed benchmark presented as measured is precisely what an actuarial review exists to catch, and it is cheaper to meet that refusal here than in the review. **Definitions behave the same way, and a rate is meaningless without its rule.** An avoidable emergency visit is a definition rather than an observation. A readmission needs both a cause rule, all-cause against condition-specific, and a time window, thirty day against ninety day, before any rate computed from it means anything, and two cases quoting different rules are not comparable even when both are correct. State the rule beside the figure, every time. ## The refusals to expect, and what each is telling you Each of these is a result, not an error, and each carries a remedy clause. The sentence it carries is usually worth more than the figure it withholds. - **The Cost of Care savings figure itself.** The wall above. It is telling you the figure you were about to compute is the one the review rejects, and that the settling evidence is adjudicated claims held by the plan, not by this world. The exposure, the funnel, the hard-soft split and the regression rate all still stand. - **A cross-payer average with no declared mix.** An unweighted mean over this corpus's payers is a number about this corpus's payer composition, not about a market. It is telling you the missing input is your own book's payer mix, which only you can declare. - **A denial rate, a payment probability, or whether a payer covers a code.** Negotiated rates are not adjudications; presence does not record a payment and absence does not record a denial. It is telling you the corpus that settles utilisation questions is not this one. - **A saving for a named payer's own book.** The modelled arithmetic runs over declared inputs; a payer's realised saving depends on their population, contracts and utilisation, none of which is here. It is telling you the figure is a model output and must travel as one. - **A pharmacy or overpayment question.** Off this world's branch of the lever tree, per the section above. It is telling you which branch the question belongs to, so it can be taken to a corpus that serves that branch. - **The sum of a deferred and an avoided total.** Never served, by design. It is telling you the two totals answer different questions and the reviewer will read a combined one as the stronger claim. ## Every answer built on this world closes with a claim report The claim report format is defined once, in the `hammer-worlds` skill. Point at it; this file does not restate it. Population, funding type and PMPM declarations are the caller's own selectors and belong under `asserted without a check`, the same way a provider attribution does in `hammer-provider-contracting`. HTTP bindings for this session: Explicit source-reference bindings: SKILL.md: https://space.hammer.ai/skills/global/building-sourced-excel-models.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm analysis.md: https://space.hammer.ai/skills/global/analysis.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm coverage-website.md: https://space.hammer.ai/skills/global/coverage-website.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm rate_position: https://space.hammer.ai/analysis/rate_position/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm rate_comparison: https://space.hammer.ai/analysis/rate_comparison/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm stack_check: https://space.hammer.ai/analysis/stack_check/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm screening_yield: https://space.hammer.ai/analysis/screening_yield/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm deferred_versus_avoided: https://space.hammer.ai/analysis/deferred_versus_avoided/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm interpretation_requirement: https://space.hammer.ai/analysis/interpretation_requirement/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm cohort_figures: https://space.hammer.ai/analysis/cohort_figures/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm comorbidity_edges: https://space.hammer.ai/analysis/comorbidity_edges/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm fee_components: https://space.hammer.ai/analysis/fee_components/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm filed_charge: https://space.hammer.ai/analysis/filed_charge/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hypertension_evidence: https://space.hammer.ai/analysis/hypertension_evidence/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm prognostic_association: https://space.hammer.ai/analysis/prognostic_association/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm treatment_effect: https://space.hammer.ai/analysis/treatment_effect/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm regression_to_mean_expectation: https://space.hammer.ai/analysis/regression_to_mean_expectation/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm competing_event_coupling: https://space.hammer.ai/analysis/competing_event_coupling/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm withhold_earnback: https://space.hammer.ai/analysis/withhold_earnback/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm tournament_position: https://space.hammer.ai/analysis/tournament_position/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm measure_reliability: https://space.hammer.ai/analysis/measure_reliability/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm declared_inputs: https://space.hammer.ai/analysis/declared_inputs/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm cost_of_care_exposure: https://space.hammer.ai/analysis/cost_of_care_exposure/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm savings_methodology: https://space.hammer.ai/analysis/savings_methodology/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hospital_variance: https://space.hammer.ai/analysis/hospital_variance/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm filer_inventory: https://space.hammer.ai/analysis/filer_inventory/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm candidate_screen: https://space.hammer.ai/analysis/candidate_screen/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm frontier: https://space.hammer.ai/analysis/frontier/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm judgement_check: https://space.hammer.ai/analysis/judgement_check/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm claim_check: https://space.hammer.ai/analysis/claim_check/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm deliver: https://space.hammer.ai/analysis/deliver/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm session_trace: https://space.hammer.ai/analysis/session_trace/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm progress_check: https://space.hammer.ai/analysis/progress_check/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm model_advisory: https://space.hammer.ai/analysis/model_advisory/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm knowledge_navigate: https://space.hammer.ai/analysis/knowledge_navigate/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm simulate: https://space.hammer.ai/analysis/simulate/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm sensitivity: https://space.hammer.ai/analysis/sensitivity/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm interval_check: https://space.hammer.ai/analysis/interval_check/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm decision_navigate: https://space.hammer.ai/analysis/decision_navigate/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm network_infer: https://space.hammer.ai/analysis/network_infer/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hello_world: https://space.hammer.ai/analysis/hello_world/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm say_world: https://space.hammer.ai/analysis/say_world/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm Read the companion references here (these replace local file references in the source guide): Build a sourced analysis: https://space.hammer.ai/analysis.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm OData exploration and analytic cuts: https://space.hammer.ai/odata.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm Worked analysis reference: https://space.hammer.ai/skills/global/analysis.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm Building sourced Excel models: https://space.hammer.ai/skills/global/building-sourced-excel-models.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm Coverage website reference: https://space.hammer.ai/skills/global/coverage-website.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm Spine cohort and post-acute analysis: https://space.hammer.ai/skills/spine-analysis.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-building-the-evidence: https://space.hammer.ai/skills/world/hammer-building-the-evidence.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-cost-of-care-case: https://space.hammer.ai/skills/world/hammer-cost-of-care-case.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-coverage-navigate: https://space.hammer.ai/skills/world/hammer-coverage-navigate.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-provider-contracting: https://space.hammer.ai/skills/world/hammer-provider-contracting.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-provider-quality: https://space.hammer.ai/skills/world/hammer-provider-quality.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-savings-classification: https://space.hammer.ai/skills/world/hammer-savings-classification.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-vendor-evidence: https://space.hammer.ai/skills/world/hammer-vendor-evidence.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm hammer-writing-the-deliverable: https://space.hammer.ai/skills/world/hammer-writing-the-deliverable.skill.txt?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm Data: https://space.hammer.ai/data/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm Transcript: https://space.hammer.ai/session/?ledger=49ec6090f32b112cd7fc2d39a72e0974&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba&workflow=spine-rtm